{"id":"W4394571530","doi":"10.1080/15376516.2024.2338907","title":"A QSAR study for predicting malformation in zebrafish embryo","year":2024,"lang":"en","type":"article","venue":"Toxicology Mechanisms and Methods","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Quantitative structure–activity relationship; Gradient boosting; Machine learning; Test set; Computer science; Artificial neural network; Multilayer perceptron; Random forest; Logistic regression; Boosting (machine learning); Set (abstract data type); Data mining; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005785545,0.0007395353,0.0005516305,0.000661888,0.0002061168,0.0003230069,0.0003474627,0.0003336663,0.001045376],"category_scores_gemma":[0.0008308162,0.0001786109,0.001128998,0.000506339,0.0002004384,0.0002566665,0.0002711722,0.0007099782,0.000216136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006476954,"about_ca_system_score_gemma":0.000756705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005739624,"about_ca_topic_score_gemma":0.005545269,"domain_scores_codex":[0.9997906,0.00003943543,0.00001214476,0.00005034516,0.00008525521,0.00002220885],"domain_scores_gemma":[0.999728,0.0001315179,0.00005450453,0.00001224608,0.0000590423,0.00001473426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005735712,0.0005871126,0.04230386,0.0008865118,0.0003287215,0.0005965414,0.0000690813,0.5180562,0.3824962,0.001800763,0.001774445,0.05052704],"study_design_scores_gemma":[0.00006282097,0.002120686,0.02755281,0.00004058735,0.0002887285,0.000205972,0.00004489938,0.833404,0.1330845,0.0006994184,0.002431119,0.0000644415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9329833,0.003716633,0.05681157,0.0004705787,0.00004305544,0.0002231414,0.003046313,0.0003977353,0.002307701],"genre_scores_gemma":[0.9771451,0.001428958,0.01865451,0.00008847515,0.000007298672,0.00008390196,0.001514217,0.00001989269,0.001057533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005739624,"threshold_uncertainty_score":0.01141244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01488848232790524,"score_gpt":0.3627183023871743,"score_spread":0.347829820059269,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}